This update formalizes public terminology for decision readiness, clarifies the distinction between intake administration and intake governance, and establishes how Lean Intake Analysis™ describes decision states without publishing proprietary scoring logic.
1. Purpose of this update
Lean Intake Analysis™ is a methodology for improving the quality, traceability, and governance of decisions made before work enters delivery. As the methodology evolves, terminology must remain stable enough for organizations, practitioners, software implementations, research, and training materials to use the same concepts consistently.
The September 2026 update establishes the public vocabulary that will be used across leanintakeanalysis.com publications. It also clarifies the boundary between the methodology itself and LIA Compass™, the software product designed to operationalize the methodology.
2. New and clarified terminology
| Term | September 2026 public definition |
|---|---|
| Decision readiness | The condition in which sufficient clarity, evidence, ownership, stakeholder context, and governance information exist to make an accountable decision about whether and how a request should advance. |
| Intake administration | The operational handling of submissions—forms, routing, required fields, queues, categorization, and status tracking. |
| Intake governance | The management discipline that determines whether a request has enough context and accountability for a decision, records the decision, and establishes conditions for what happens next. |
| Evidence-backed decision | A decision whose rationale can be connected to stated facts, assumptions, dependencies, risks, constraints, and stakeholder input rather than only to undocumented preference or momentum. |
| Decision condition | A requirement, dependency, owner action, threshold, or unresolved item that must be satisfied, monitored, or revisited when a request advances. |
| Decision record | A concise, traceable record of the request, rationale, decision state, conditions, accountable owners, and relevant review point. |
| Hold | An active governance decision indicating that a request should not advance yet because a material condition for decision or commitment remains unresolved. |
3. What decision readiness is—and is not
Decision readiness is a threshold for making the next responsible decision. It is not a promise that delivery will be easy, risk-free, or fully planned. It does not require complete certainty, detailed project planning, a finished business case, or a final technical design in every circumstance. The required level of evidence should be proportionate to the consequence of the decision.
This distinction is especially important for exploratory and innovative work. A small experiment may be decision-ready with a light evidence burden because the organizational commitment is limited. A customer-facing AI agent, regulated-data initiative, major capital investment, or enterprise platform decision should require more explicit evidence and governance because the consequences of an incorrect commitment are higher.
4. The public decision-readiness model
Beginning with version 2026.09, public LIA publications will describe decision readiness through seven dimensions. These dimensions support communication and education. They do not reveal the LIA proprietary scoring formula or controlled certification artifacts.
- Problem clarity: Is the need or opportunity clear enough to make a decision about it?
- Strategic and value context: Why does the request matter to the organization or its stakeholders?
- Ownership and accountability: Who owns the outcome and the decision?
- Stakeholder alignment: Who is materially affected, and are known disagreements visible?
- Evidence and assumptions: What is known, assumed, uncertain, or still to be tested?
- Risk and constraint context: Which material constraints or risk domains could change the decision?
- Decision and governance conditions: What decision is being made, under what conditions, by whom, and what must happen next?
5. Decision states
LIA recognizes that enterprise decisions are not always binary. The methodology therefore supports decision states that distinguish permission to proceed from the conditions attached to that permission.
| Decision state | Meaning |
|---|---|
| Proceed | The organization has enough evidence and accountability to authorize the next defined step. |
| Proceed with conditions | The request may advance, but explicit conditions, owners, or review points remain attached to the decision. |
| Hold | The request should not advance yet; a material readiness condition must be resolved before another decision is made. |
| Stop / Do not proceed | The current request should not continue because the evidence, value, risk, feasibility, duplication, or strategic context does not support commitment in its present form. |
The decision state describes what the organization has chosen. It is separate from the readiness score or assessment logic used to support that choice. Human decision authority remains explicit.
6. A Hold is not a failed intake
This update formally treats Hold as a valid governance outcome. A mature intake system should not reward throughput at the expense of decision quality. If a request lacks a clear owner, depends on unresolved architecture, contains an unaccepted compliance exposure, or requires evidence that has not yet been produced, advancing it can transfer unresolved decision work into delivery.
A Hold should therefore include three elements: the reason the request cannot responsibly advance, the condition that must change, and the person or role accountable for the next action. A Hold without these elements becomes passive waiting; a Hold with them becomes governed work.
7. Methodology versus software
Lean Intake Analysis™ is the methodology. LIA Compass™ is software designed to operationalize the methodology. Organizations can apply LIA concepts without using LIA Compass. The software provides workflow, records, structured assessment, and decision-support capabilities; it does not replace human accountability or the methodology itself.
This distinction is intentionally maintained across future publications. Product functionality may evolve more quickly than the underlying methodology. Conversely, methodology updates may clarify terminology or governance practice without requiring an immediate software change.
8. What has not changed
- LIA is not intended to create approval bureaucracy for low-risk, low-consequence work.
- LIA does not replace portfolio prioritization, project management, product management, architecture, risk management, or financial governance.
- LIA does not require every request to have perfect information before a decision can be made.
- LIA does not transfer human decision authority to an algorithm or score.
- The detailed scoring method, controlled worksheets, certification exercises, and certain decision-engine logic remain proprietary and are not published in the public methodology update series.
9. External alignment
The September update is consistent with broader governance trends. NIST's AI RMF emphasizes understanding context, purpose, benefits, costs, risks, assumptions, and human oversight before initial go/no-go decisions about AI systems. PMI's current project-management guidance emphasizes governance, value, accountability, stakeholders, resources, and risk as connected management domains. LIA applies a similar principle further upstream: before delivery governance can be effective, the decision creating the work should itself be explicit and traceable (NIST, 2023; PMI, 2025).
10. Versioning and next update
This publication establishes methodology version 2026.09 for public-facing terminology. Future updates will identify additions, clarifications, deprecations, and changes that materially affect how LIA is described or applied. The next planned methodology topic is decision rights, conditional approval, escalation, and re-review.
References
- National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf
- Project Management Institute. (2025). PMBOK Guide - Eighth Edition. https://www.pmi.org/standards/pmbok